arXiv research

New adaptive temperature selection improves parallel tempering efficiency.

A new method selects optimal temperature for Bayesian Deep Learning.

The paper prices weather contracts using a complex temperature model.

Improves overparameterized models' robustness to distribution shifts.

Attention temperature improves robustness of ICL in high-dimensional settings.

AEW estimator achieves optimal risk in expectation for large enough temperatures.

Study compares machine learning algorithms for predicting SST in the Great Barrier Reef.

Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task for several decades now, as signal injection or sensor-based methods still prove unfeasible in a commercial context. Overheating results in severe motor deterioration and is thus of high co…

Post-hoc transforms can reverse model performance trends, especially in noisy settings.

Method reduces model bias in water temperature prediction using physics-guided GNNs.

The effects of weather on agriculture in recent years have become a major global concern. Hence, the need for an effective weather risk management tool (i.e., weather derivatives) that can hedge crop yields against weather uncertainties. However, most smallholder farmers and agricultural stakeholders are unwilling to p…

AdaAnn optimizes annealing for efficient probability density approximation.

A model predicts how hyperparameters affect pruning performance.

We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation. The model considered can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach…

Thermal preferences vary from person to person and may change over time. The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optimally learn the indoor air temperature values which maximize their satisfaction. Our central hypothesis is that an occupant's preference rela…

Temperature scaling improves model uncertainty but not diversity in LLMs.

Introduce a thermodynamically informed, temperature-transferable MLCG framework for proteins.

Water managers in the western United States (U.S.) rely on longterm forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. To improve the accuracy of these longterm forecasts, the U.S. Bureau of Reclamation and the National Oceanic and Atmospheric Administration (NOAA) launch…

Study local sensitivity of HDD and CDD temperature derivatives prices.

We introduce a temperature into the exponential function and replace the softmax output layer of neural nets by a high temperature generalization. Similarly, the logarithm in the log loss we use for training is replaced by a low temperature logarithm. By tuning the two temperatures we create loss functions that are non…

Climate volatility reduces economic growth, especially in poorer countries.

Logit-GFN accelerates GFlowNets training by scaling logits based on temperature.

This paper measures temperature in agent systems using volatility.

Auto-Compressing Subset Pruning reduces model size for faster inference.

Trends in terrestrial temperature variability are perhaps more relevant for species viability than trends in mean temperature. In this paper, we develop methodology for estimating such trends using multi-resolution climate data from polar orbiting weather satellites. We derive two novel algorithms for computation that …

The paper analyzes extreme temperature forecasting using machine learning models.

We recapitulate the Bayesian formulation of neural network based classifiers and show that, while sampling from the posterior does indeed lead to better generalisation than is obtained by standard optimisation of the cost function, even better performance can in general be achieved by sampling finite temperature ($T$) …

The paper analyzes the InfoNCE loss under different temperature schedules using Langevin dynamics.

The Gibbs algorithm's generalization error is bounded, improving with prior volume in low temperatures.

Optimizes contrastive learning with individualized temperatures for better performance on imbalanced datasets.

Study evaluates different mathematical models for three case studies using statistical fitting.

The present study provides a comparative assessment of non-invasive sensors as means of estimating the microbial contamination and time-on-shelf (i.e. storage time) of leafy green vegetables, using a novel unified spectra analysis workflow. Two fresh ready-to-eat green salads were used in the context of this study for …

New method uses RBM flows to find critical temperatures in Ising models.

Algorithm learns Sherrington-Kirkpatrick model parameters at low temperatures.

Develops a new stochastic volatility model for temperature derivatives.

Evaluation of the marginal likelihood plays an important role in model selection problems. The widely applicable Bayesian information criterion (WBIC) and singular Bayesian information criterion (sBIC) give approximations to the log marginal likelihood, which can be applied to both regular and singular models. When the…

Study shows sample complexity for logistic regression with normal covariates.

The minute fluctuations of of S&P 500 and NASDAQ 100 indices display Boltzmann statistics over a wide range of positive as well as negative returns, thus allowing us to define a {\em market temperature} for either sign. With increasing time the sharp Boltzmann peak broadens into a Gaussian whose volatility $σ$ measure…

In the spirit of behavioral finance, we study the process of opinion formation among investors using a variant of the 2D Voter Model with a tunable social temperature. Further, a feedback acting on the temperature is introduced, such that social temperature reacts to market imbalances and thus becomes time dependent. I…

A spin model relating physical to financial variables is presented. This work is the first to introduce the concept of negative absolute temperature into stock market dynamics by establishing a rigorous formal analogy between physical and financial variables. Based on this model, an algorithm evaluating negative temper…

Machine learning and deep learning infer surface/groundwater exchange from temperature data.

Researchers derive the relation between temperature and volatility in ideal agent systems.

We introduce the concrete autoencoder, an end-to-end differentiable method for global feature selection, which efficiently identifies a subset of the most informative features and simultaneously learns a neural network to reconstruct the input data from the selected features. Our method is unsupervised, and is based on…

New method estimates Nishimori temperature for node classification in weighted graphs.

Cold posteriors improve Bayesian neural networks by reducing overestimation of aleatoric uncertainty.

Temperature scaling fails for distributions with class overlaps, while Mixup improves calibration.

Understanding local currents in the North Atlantic region of the ocean is a key part of modelling heat transfer and global climate patterns. Satellites provide a surface signature of the temperature of the ocean with a high horizontal resolution while in situ autonomous probes supply high vertical resolution, but horiz…

We present a model of financial markets originally proposed for a turbulent flow, as a dynamic basis of its intermittent behavior. Time evolution of the price change is assumed to be described by Brownian motion in a power-law potential, where the `temperature' fluctuates slowly. The model generally yields a fat-tailed…